OpenRaiser/Intern-Atlas huggingface dataset:
Distilling On-device Language Models for Robot Planning with Minimal Human Intervention
Venue: arXiv cs.RO 2025
Year: 2025

Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or industrial settings. We present PRISM, a framework for distilling small language model (SLM)-enabled robot planners that run on-device with minimal human supervision. Starting from an existing LLM-enabled planner, PRISM automatically synthesizes diverse tasks and environments, elicits plans from the LLM, and uses this synthetic dataset to distill a compact SLM as a drop-in replacement of the source model. We apply PRISM to three LLM-enabled planners for mapping and exploration, manipulation, and household assistance, and we demonstrate that PRISM improves the performance of Llama-3.2-3B from 10-20% of GPT-4o's performance to over 93% - using only synthetic data. We further demonstrate that the distilled planners generalize across heterogeneous robotic platforms (ground and aerial) and diverse environments (indoor and outdoor). We release all software, trained models, and datasets at https://zacravichandran.github.io/PRISM.
From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training
Venue: arXiv cs.SD 2025
Year: 2025

Despite progress in controllable symbolic music generation, data scarcity remains a challenge for certain control modalities. Composer-style music generation is a prime example, as only a few pieces per composer are available, limiting the modeling of both styles and fundamental music elements (e.g., melody, chord, rhythm). In this paper, we investigate how general music knowledge learned from a broad corpus can enhance the mastery of specific composer styles, with a focus on piano piece generation. Our approach follows a two-stage training paradigm. First, we pre-train a REMI-based music generation model on a large corpus of pop, folk, and classical music. Then, we fine-tune it on a small, human-verified dataset from four renowned composers, namely Bach, Mozart, Beethoven, and Chopin, using a lightweight adapter module to condition the model on style indicators. To evaluate the effectiveness of our approach, we conduct both objective and subjective evaluations on style accuracy and musicality. Experimental results demonstrate that our method outperforms ablations and baselines, achieving more precise composer-style modeling and better musical aesthetics. Additionally, we provide observations on how the model builds music concepts from the generality pre-training and refines its stylistic understanding through the mastery fine-tuning.
Deep-OFDM: Neural Modulation for High Mobility
Venue: arXiv cs.IT 2025
Year: 2025

Orthogonal Frequency Division Multiplexing (OFDM) is the foundational waveform in current 5G deployments due to its robustness in quasi-static channels and efficient spectrum use. However, in high-mobility scenarios, OFDM suffers from inter-carrier interference (ICI), and its reliance on dense pilot patterns and cyclic prefixes significantly reduces spectral efficiency. In this work, we propose Deep-OFDM: a learnable modulation framework that augments traditional OFDM by incorporating neural parameterization. Instead of mapping each symbol to a fixed resource element, Deep-OFDM spreads information across the OFDM grid using a convolutional neural network modulator. This modulator is jointly optimized with a neural receiver through end-to-end training, enabling the system to adapt to time-varying channels without relying on explicit channel estimation. Deep-OFDM outperforms conventional OFDM when paired with neural receiver baselines, particularly in pilot-sparse and pilotless regimes, achieving substantial gains in BLER and goodput, especially at high Doppler frequencies. In the pilotless setting, the neural modulator learns a low-rank structure that resembles a superimposed pilot, effectively enabling reliable communication without explicit overhead. Deep-OFDM demonstrates significant improvements in BLER and goodput at high Doppler frequencies across various scenarios, including MIMO systems. Comprehensive ablation studies quantify the role of nonlinear activations and characterize performance-complexity trade-offs. These results highlight the potential of transmitter-receiver co-design for robust, resource-efficient communication, paving the way for AI-native physical layer designs in next-generation wireless systems.
DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for Mathematical Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we propose DuaShepherd, a novel reward modeling framework that integrates two complementary reward signals, correctness and potential, to enhance the mathematical reasoning capabilities of Large Language Models (LLMs). While correctness-based signals emphasize identification of stepwise errors, potential-based signals focus on the likelihood of reaching the correct final answer. We developed an automated pipeline for constructing large-scale reward modeling dataset with both signals. A unified, multi-head architecture was explored to train the two reward models in a multi-task setup, demonstrating benefits from learning both correctness and potential in parallel. By combining these two signals into a compound probability, our model achieves consistent performance improvements across multiple benchmarks. Empirical evaluations on MATH500 and ProcessBench confirm that this combined reward significantly outperforms models trained on either reward type alone, achieving state-of-the-art performance under comparable resource constraints.
Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning without 3D Annotations
Venue: arXiv cs.CV 2025
Year: 2025

Currently, utilizing large language models to understand the 3D world is becoming popular. Yet existing 3D-aware LLMs act as black boxes: they output bounding boxes or textual answers without revealing how those decisions are made, and they still rely on pre-trained 3D detectors to supply object proposals. We introduce Scene-R1, a video-grounded framework that learns to reason about 3D scenes without any point-wise 3D instance supervision by pairing reinforcement-learning-driven reasoning with a two-stage grounding pipeline. In the temporal grounding stage, we explicitly reason about the video and select the video snippets most relevant to an open-ended query. In the subsequent image grounding stage, we analyze the image and predict the 2D bounding box. After that, we track the object using SAM2 to produce pixel-accurate masks in RGB frames, and project them back into 3D, thereby eliminating the need for 3D detector-based proposals while capturing fine geometry and material cues. Scene-R1 can also adapt to the 3D visual question answering task to answer free-form questions directly from video. Our training pipeline only needs task-level 2D boxes or textual labels without dense 3D point-wise labels. Scene-R1 surpasses existing open-vocabulary baselines on multiple datasets, while delivering transparent, step-by-step rationales. These results show that reinforcement-learning-based reasoning combined with RGB-D video alone offers a practical, annotation-efficient route to trustworthy 3D scene understanding.
Joint Transmission for Cellular Networks with Pinching Antennas: System Design and Analysis
Venue: arXiv cs.IT 2025
Year: 2025

As an emerging flexible antenna technology for wireless communications, pinching-antenna systems, offer distinct advantages in terms of cost efficiency and deployment flexibility. This paper investigates joint transmission strategies of the base station (BS) and pinching antennas (PAS), focusing specifically on how to cooperate efficiently between the BS and waveguide-mounted pinching antennas for enhancing the performance of the user equipment (UE). By jointly considering the performance, flexibility, and complexity, we propose three joint BS-PAS transmission schemes along with the best beamforming designs, namely standalone deployment (SD), semi-cooperative deployment (SCD) and full-cooperative deployment (FCD). More specifically, for each BS-PAS joint transmission scheme, we conduct a comprehensive performance analysis in terms of the power allocation strategy, beamforming design, and practical implementation considerations. We also derive closed-form expressions for the average received SNR across the proposed BS-PAS joint transmission schemes, which are verified through Monte Carlo simulations. Finally, numerical results demonstrate that deploying pinching antennas in cellular networks, particularly through cooperation between the BS and PAS, can achieve significant performance gains. We further identify and characterize the key network parameters that influence the performance, providing insights for deploying pinching antennas.
Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option
Venue: arXiv cs.RO 2025
Year: 2025

Safe, reliable navigation in extreme, unfamiliar terrain is required for future robotic space exploration missions. Recent generative-AI methods learn semantically aware navigation policies from large, cross-embodiment datasets, but offer limited safety guarantees. Inspired by human cognitive science, we propose a risk-guided diffusion framework that fuses a fast, learned "System-1" with a slow, physics-based "System-2", sharing computation at both training and inference to couple adaptability with formal safety. Hardware experiments conducted at the NASA JPL's Mars-analog facility, Mars Yard, show that our approach reduces failure rates by up to $4\times$ while matching the goal-reaching performance of learning-based robotic models by leveraging inference-time compute without any additional training.
EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration
Venue: arXiv cs.LG 2025
Year: 2025

While Large Language Models (LLMs) have become highly influential, their enormous scale presents significant deployment challenges. Efficiently serving these models typically requires distributing them across numerous accelerator devices, which introduces substantial performance overhead from inter-device communication (collectives). While model quantization has been widely adopted to reduce the memory and compute requirements of LLM weights and activations with minimal quality impact, applying quantization directly to collectives like AllReduce is inherently difficult due to the inter-device summation involved, which can lead to numerical instability or significant error accumulation. In this work, we present a native dynamic block-wise efficient quantized AllReduce within the XLA compiler for TPUs (EQuARX). By using TPU-friendly quantization and deep pipelining of communication and compute, EQuARX with int8 precision achieves a 1.8X speedup over baseline BF16 AllReduce across various network topologies. Furthermore, EQuARX accelerates the prefill stage of Gemma 3 27B by 1.25X and Gemma 3 12B by 1.1X, respectively, with small to negligible impact on quality.
List-Decodable Byzantine Robust PIR: Lower Communication Complexity, Higher Byzantine Tolerance, Smaller List Size
Venue: arXiv cs.CR 2025
Year: 2025

Private Information Retrieval (PIR) is a privacy-preserving primitive in cryptography. Significant endeavors have been made to address the variant of PIR concerning the malicious servers. Among those endeavors, list-decodable Byzantine robust PIR schemes may tolerate a majority of malicious responding servers that provide incorrect answers. In this paper, we propose two perfect list-decodable BRPIR schemes. Our schemes are the first ones that can simultaneously handle a majority of malicious responding servers, achieve a communication complexity of $o(n^{1/2})$ for a database of size n, and provide a nontrivial estimation on the list sizes. Compared with the existing solutions, our schemes attain lower communication complexity, higher byzantine tolerance, and smaller list size.
Empowering Iterative Graph Alignment Using Heat Diffusion
Venue: arXiv cs.SI 2025
Year: 2025

Unsupervised plain graph alignment (UPGA) aims to align corresponding nodes across two graphs without any auxiliary information. Existing UPGA methods rely on structural consistency while neglecting the inherent structural differences in real-world graphs, leading to biased node representations. Moreover, their one-shot alignment strategies lack mechanisms to correct erroneous matches arising from inaccurate anchor seeds. To address these issues, this paper proposes IterAlign, a novel parameter-free and efficient UPGA method. First, a simple yet powerful representation generation method based on heat diffusion is introduced to capture multi-level structural characteristics, mitigating the over-reliance on structural consistency and generating stable node representations. Two complementary node alignment strategies are then adopted to balance alignment accuracy and efficiency across graphs of varying scales. By alternating between representation generation and node alignment, IterAlign iteratively rectifies biases in nodes representations and refines the alignment process, leading to superior and robust alignment performance. Extensive experiments on three public benchmarks demonstrate that the proposed IterAlign outperforms state-of-the-art UPGA approaches with a lower computational overhead, but also showcases the ability to approach the theoretical accuracy upper bound of unsupervised plain graph alignment task.
Quantizing for Noisy Flash Memory Channels
Venue: arXiv cs.IT 2025
Year: 2025

Flash memory-based processing-in-memory (flash-based PIM) offers high storage capacity and computational efficiency but faces significant reliability challenges due to noise in high-density multi-level cell (MLC) flash memories. Existing verify level optimization methods are designed for general storage scenarios and fail to address the unique requirements of flash-based PIM systems, where metrics such as mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are critical. This paper introduces an integrated framework that jointly optimizes quantization and verify levels to minimize the MSE, considering both quantization and flash memory channel errors. We develop an iterative algorithm to solve the joint optimization problem. Experimental results on quantized images and SwinIR model parameters stored in flash memory show that the proposed method significantly improves the reliability of flash-based PIM systems.
DRST: a Non-Intrusive Framework for Performance Analysis in Softwarized Networks
Venue: arXiv cs.NI 2025
Year: 2025

The last decade has witnessed the proliferation of network function virtualization (NFV) in the telco industry, thanks to its unparalleled flexibility, scalability, and cost-effectiveness. However, as the NFV infrastructure is shared by virtual network functions (VNFs), sporadic resource contentions are inevitable. Such contention makes it extremely challenging to guarantee the performance of the provisioned network services, especially in high-speed regimes (e.g., Gigabit Ethernet). Existing solutions typically rely on direct traffic analysis (e.g., packet- or flow-level measurements) to detect performance degradation and identify bottlenecks, which is not always applicable due to significant integration overhead and system-level constraints. This paper complements existing solutions with a lightweight, non-intrusive framework for online performance inference that easily adapts to drift (i.e., a change over time of the actual state of our system). Instead of direct data-plane collection, we reuse hardware features in the underlying NFV infrastructure, introducing negligible interference in the data-plane. Our Drift-Resilient and Self-Tuning (DRST) framework can be integrated into existing NFV systems with minimal engineering effort and operate without the need for predefined traffic models or VNF-specific customization. DRST is deployed via a lightweight MLOps pipeline that automates the adaptation under runtime drift. We show how DRST can deliver accurate performance inference or diagnose run-time bottlenecks, as demonstrated through a comprehensive evaluation across diverse NFV scenarios.
MDSAM:Memory-Driven Sparse Attention Matrix for LVLMs Hallucination Mitigation
Venue: arXiv cs.CV 2025
Year: 2025

Hallucinations in large vision-language models (LVLMs) often stem from the model's sensitivity to image tokens during decoding, as evidenced by attention peaks observed when generating both real and hallucinated entities. To address this, we propose Memory-Driven Sparse Attention Matrix (MDSAM) , a novel training-free approach that dynamically captures and refines the attention allocated to image tokens at each layer. MDSAM memorizes attention patterns and activates updates through alignment during decoding, enhancing focus on relevant image tokens while effectively reducing hallucinations. We evaluate MDSAM on multiple benchmarks for tasks such as image captioning and visual question answering, demonstrating its ability to consistently reduce hallucinations and improve reliability. Compatible with various LVLM architectures, MDSAM highlights its adaptability and effectiveness in mitigating hallucinations without requiring additional training or external tools.
TPTT: Transforming Pretrained Transformers into Titans
Venue: arXiv cs.CL 2025
Year: 2025

Transformer-based large language models (LLMs) have achieved strong performance across many natural language processing tasks. Nonetheless, their quadratic computational and memory requirements, particularly in self-attention layers, pose challenges for efficient inference on long contexts and for deployment in resource-limited environments. We present TPTT (Transforming Pretrained Transformers into Titans), a framework designed to augment pretrained Transformers with linearized attention (LiZA) and internal memory gating via Memory as Gate (MaG), applied without full retraining. TPTT supports parameter-efficient fine-tuning (LoRA) and integrates with standard toolkits such as Hugging Face Transformers. We evaluated TPTT on several pretrained models, including Llama-1B, OlMoE-1B-7B, Qwen2.5-1.5B, Gemma3-270m, OpenELM-1.3B, and Mistral-7B, in order to assess applicability across architectures of different scales. Experiments on models with approximately 1 billion parameters, evaluated primarily on the MMLU benchmark, suggest potential improvements in both efficiency and accuracy compared to baseline models. For example, Titans-Llama-1B exhibited up to a 20\% relative increase in Exact Match scores in one-shot evaluation. An additional finding is that it is possible to convert a quadratic-attention model into a purely linear-attention model using the DeltaProduct mechanism. All training runs were carried out with modest computational resources. These preliminary findings indicate that TPTT may help adapt pretrained LLMs for long-context tasks with limited overhead. Further studies on larger models and a broader set of benchmarks will be necessary to evaluate the generality and robustness of the framework. Code is available at https://github.com/fabienfrfr/tptt . Python package at https://pypi.org/project/tptt/ .
Learning Personalized Utility Functions for Drivers in Ride-hailing Systems Using Ensemble Hypernetworks
Venue: arXiv cs.LG 2025
Year: 2025

In ride-hailing systems, drivers decide whether to accept or reject ride requests based on factors such as order characteristics, traffic conditions, and personal preferences. Accurately predicting these decisions is essential for improving the efficiency and reliability of these systems. Traditional models, such as the Random Utility Maximization (RUM) approach, typically predict drivers' decisions by assuming linear correlations among attributes. However, these models often fall short because they fail to account for non-linear interactions between attributes and do not cater to the unique, personalized preferences of individual drivers. In this paper, we develop a method for learning personalized utility functions using hypernetwork and ensemble learning. Hypernetworks dynamically generate weights for a linear utility function based on trip request data and driver profiles, capturing the non-linear relationships. An ensemble of hypernetworks trained on different data segments further improve model adaptability and generalization by introducing controlled randomness, thereby reducing over-fitting. We validate the performance of our ensemble hypernetworks model in terms of prediction accuracy and uncertainty estimation in a real-world dataset. The results demonstrate that our approach not only accurately predicts each driver's utility but also effectively balances the needs for explainability and uncertainty quantification. Additionally, our model serves as a powerful tool for revealing the personalized preferences of different drivers, clearly illustrating which attributes largely impact their rider acceptance decisions.
Location Information Sharing Using Software Defined Radio in Multi-UAV Systems
Venue: arXiv cs.NI 2025
Year: 2025

SDR (Software Defined Radio) provides flexible, reproducible, and longer-lasting radio tools for military and civilian wireless communications infrastructure. SDR is a radio communication system whose components are implemented as software. This study aims to establish multi-channel wireless communication with FANET between two SDRs to share location information and examine it in a realistic test environment. We used multi-channel token circulation as a channel access protocol and GNU Radio platform for SDR software development. The structures of the communication layer, including the protocols, communication systems, and network structures suggested in the studies in the literature, are generally tested in the simulation environment. The simulation environment provides researchers with fast and easy development and testing, but disadvantages exist. These cause a product to be isolated from hardware, software, and cost effects encountered while developing and environmental factors affecting the communication channel while testing. Another contribution of the study is to present the developed block diagrams and codes as clear and reproducible. The developed software and block diagrams are available at github.com/knrl/uav-in-802.11-gnuradio.
Reinforcing User Interest Evolution in Multi-Scenario Learning for recommender systems
Venue: arXiv cs.IR 2025
Year: 2025

In real-world recommendation systems, users would engage in variety scenarios, such as homepages, search pages, and related recommendation pages. Each of these scenarios would reflect different aspects users focus on. However, the user interests may be inconsistent in different scenarios, due to differences in decision-making processes and preference expression. This variability complicates unified modeling, making multi-scenario learning a significant challenge. To address this, we propose a novel reinforcement learning approach that models user preferences across scenarios by modeling user interest evolution across multiple scenarios. Our method employs Double Q-learning to enhance next-item prediction accuracy and optimizes contrastive learning loss using Q-value to make model performance better. Experimental results demonstrate that our approach surpasses state-of-the-art methods in multi-scenario recommendation tasks. Our work offers a fresh perspective on multi-scenario modeling and highlights promising directions for future research.